I recently read a good post by Liam Gooding on 5 Steps to Becoming a Data Driven Organisation. It’s worth a read, especially to see him perform a no-holds-barred take down of his prospect (a self-declared data driven evangelist). People think I’m direct. I’ve got nothing on this guy!

But seriously, Gooding does make a good point about the difference between BEING data driven and SAYING your data driven. It’s much easier to say it than do it.

At the same time, from my perspective, you have to keep in mind the difference between Gooding’s startup/technology customers and our mid-market manufacturing/distribution clients. Ninety-nine percent of the time, we’re helping our clients figure out how to make money and handle their complex accounting challenges. Generally, becoming truly data driven isn’t within our mandate.

That said, Gooding does raise one point I’d like to discuss further: the value of using multiple analytics tools. Gooding is all for it. He makes the point that employees operate at different levels when it comes to data. So you need multiple tools suited to multiple audiences.

Now, as readers of this blog, you’ll know that after years of working with dozens of different tools, we now focus almost exclusively on Microsoft Business Intelligence Stack (Excel, SSRS, Power BI, etc.). That doesn’t mean that we fundamentally disagree with Gooding. But we do believe that for our clients (and the types of problems we solve), other tools aren’t necessary.

If a client does want to use multiple tools (and it’s true that some tools are better for certain applications), we make sure they understand what they’re getting into. Because using multiple tools comes with its own set of problems.

In that spirit, here are four problems that can result from using multiple business intelligence products:

1. Multiple Systems Doesn’t Mean an Optimal Solution

It’s true that different tools are good at different things. But when organizations implement multiple tools, it’s rarely because that’s the optimal way to solve the problem. Usually, it’s because they’re desperate to get a solution in place, and IT wasn’t responding. So multiple systems became the answer (if not the best answer).

2. Every Tool Will Require In-House Technical Support

“But not SaaS,” you say. “It runs in any browser. That’s what the salesperson said.”

Ah, the lies of software salespeople. You can add this one to “your users will write their own reports.”

Case in point:  A client signed up for a SaaS BI solution. That solution, to be delivered over a browser, required changes to the browser setup that conflicted with other settings the company needed for security reasons. If you’re using different tools in the same browser, getting all the settings to work together can be a major pain.

3. The More Solutions, the Greater the Security Risk

I totally get that data security is an excuse some IT departments use to keep things under control (and to prevent progress, it seems). But that’s not to say data security isn’t an issue. And when departments look for solutions, they almost always ignore data security risks.

4. It Creates Data Silos

Gooding acknowledges this problem with a large caveat:

One word of warning: be careful to not create more data silos. Make it clear that any new data tracked should eventually have a path into the organisations central data repository. This means buying into tools with modern developer API’s and ensuring data is kept clean.

Our experience at Red Three is that data silos almost automatically result when departments use different tools. Why? Because getting the data back into the central repository is hard work and requires centralized resources—the same centralized resources that were unavailable in the first place, which led people to implement multiple solutions. See the cycle?

What’s your experience? Have different solutions delivered real benefits for you? Or did you wind up having multiple answers to the same questions?

 

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